Android application for chest x-ray health classification from a CNN deep learning TensorFlow model
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Document Type
Conference Proceeding
Source Title
2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech)
First Page
255
Last Page
259
Publication Date
2020
Abstract
One of the problems in the medical field is incorrect diagnosis, particularly overdiagnosis and underdiagnosis. One of the illnesses that is currently researched upon is pneumonia. Several methodologies are employed to further validate this diagnosis. Often, to achieve the goal, medical experts rely on an X-ray image. In this study, the basis is still x-ray images with the incorporation of image processing and machine learning. MobileNetV2 is utilized as the convolution neural network model. The produced frozen graph is injected to Android Studio to produce an android mobile application which will serve as a diagnostic tool. The mobile application has high accuracy and considered reliable because of testing and validation results. This study generally aims to provide a reliable low-cost aid for medical professionals in diagnosing pneumonia. © 2020 IEEE.
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Digitial Object Identifier (DOI)
10.1109/LifeTech48969.2020.1570619189
Recommended Citation
Tobias, R. N., De Jesus, L. M., Mital, M. G., Lauguico, S. C., Guillermo, M., Sybingco, E., Bandala, A. A., & Dadios, E. P. (2020). Android application for chest x-ray health classification from a CNN deep learning TensorFlow model. 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), 255-259. https://doi.org/10.1109/LifeTech48969.2020.1570619189
Disciplines
Electrical and Computer Engineering | Electrical and Electronics | Systems and Communications
Keywords
Diagnosis, Radioscopic; Pneumonia—Diagnosis; Application software; Neural networks (Computer science); Image processing
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